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OPEN FAU

Online publication system of Friedrich-Alexander-Universität Erlangen-Nürnberg

The online publication system OPEN FAU is the central publication platform for Open Access publishing for all members of Friedrich-Alexander-Universität. Qualified works from research and teaching may be published here free of charge, either as a primary or secondary publication. The full texts are permanently available worldwide and are findable and citable via catalogues and search engines.


To search for documents in OPEN FAU, please select "Search" (via the magnifying glass at the top right); this will provide you with various search options. If you want to publish a document, go to "Login" and "My Publications". Then drag you document into the field provided and enter the metadata. In just a few steps, you can submit your document. Please note our guidelines, the publication contract and FAQs.

 

Recent Submissions

Bachelor thesis
Open Access
Aufbau und Erprobung eines Versuchsstands zur berührungslosen Handhabung flüssiger und fester Objekte mit Ultraschall
Proceedings of the Institute for Multiscale Simulation (2018-10-04) Massa, Jonas; Sack, Achim
Bachelor thesis
Open Access
Röntgentomographische Charakterisierung von Wurzelwachstum in granularer Materie
Proceedings of the Institute for Multiscale Simulation (2017-10-29) Zoller, Doris; Schroeter, Matthias
Master thesis
Open Access
Pattern formation in a horizontally vibrated granular submonolayer
Proceedings of the Institute for Multiscale Simulation (2012-01-26) Krengel, Dominik; Pöschel, Thorsten
In 1995, Strassburger et al [27] carried out an experiment in which they shook a sub-monolayer of particles horizontally. Their investigations revealed that particles arranged themselves in stripes under the influence of the shaking. To describe this behavior, they developed a simple cellular automaton model: during an impact, all particles jump simultaneously in the direction of impact until they come to rest on a left and a right neighbor. However, the jumping could never be observed experimentally, suggesting that the streaking has a different cause. The aim of this work is to identify the key mechanism for streak formation in a horizontally shaken submonolayer of particles. For this purpose, the experiment of Strassburger et al. is repeated to confirm the effect. Based on the experiment, a force-based multiparticle simulation is performed to specifically investigate the system. It is shown that although particles can jump during the simulation, this is not essential for their streak formation. Friction between particles can be identified as the basis for streak formation, which means that particles in contact can no longer roll freely and move together with the bottom of the shaking container. Furthermore, it is found that streaking only occurs at higher excitation amplitudes and lower excitation frequencies. Other combinations lead to the particles in the system either not being supplied with enough energy to form streaks, or receiving too much energy and assuming a gas-like state. A comparison between the experiment and simulation shows that the non-round particles used in the experiment, which can only roll to a limited extent, lead to deformed stripes within which the particles are quasi-stationary. In the simulation, on the other hand, perfectly round spheres were used and strips with low deformation were generated, within which the particles exhibit high mobility. During the preparation of the simulation, collisions of three-dimensional particles were analyzed. It was found that the normal restitution coefficient can assume negative values. This effect was already known for high-speed impacts of nanoparticles [52]. However, it can be shown that this effect is a general phenomenon resulting from the geometry of the impact. Negative restitution coefficients can be observed for all types of collisions that are determined by finite interaction forces.
Master thesis
Open Access
The Dzhanibekov-Effect - an intuitive theoretical and practical approach to understand the rotation of rigid bodies about their intermediate axis
Proceedings of the Institute for Multiscale Simulation (2023-06-16) Aseervatham, Bijohn Chandrew; Buchele, Felix
Master thesis
Open Access
Charakterisierung poröser Medien mittels Machine Learning in Python mit scikit−learn, Tensorflow und tflearn
Proceedings of the Institute for Multiscale Simulation (2018-09-24) Hall, Alexander; Schroeter, Matthias
Diese Arbeit untersucht einen Ansatz zur Charakterisierung von porösen Medien mit Hilfe von Machine Learning in Python anhand von Sandsteinproben. Hierfür wurden dreidimensionale Computertomogramme der Sandsteine Bentheimer und Castelgate in verschiedenen Auflösungen aufgenommen und die Porenstruktur in Porensegmente unterteilt. Die Segmentierung erfolgte nach zwei Ansätzen. Im ersten Ansatz wurden, an zufälligen Stellen im Tomogramm, Würfel mit verschiedenen definierten Kantenlängen ausgeschnitten. Dabei wurde nur der Porenanteil des Würfels berücksichtigt. Im zweiten Ansatz wurde das Porensystem systematisch mit Hilfe des Watershed Algorithmus segmentiert. Die einzelnen Porensegmente wurden anhand des Konzepts der Minkowski Tensoren mittels des Programms Bilimbi quantifiziert und als Feature für die drei verschiedenen Klassifikationsalgorithmen: Neuronale Netzwerke, C Support Vector Machine und Random Forrest verwendet. Vor der Klassifikation erfolgte eine Optimierung der Algorithmenparameter. Weiterhin wurde der Einfluss jeweiligen Minkowski Tensoren als Feature auf die Klassifikation mit Hilfe von Selektionsalgorithmen untersucht. Die Aussagen der Selektionsalgorithmen wurden anschließend durch eine Klassifikation der einflussreichsten Feature überprüft. Die präsentierte Methode ermöglicht die Untersuchung des Einflusses der Porenstruktur auf den Low-salinity Effekt von Sandsteinen und die Einstufung der Eignung potentieller öltragender Gesteinschichten für die Ölfördermethode des Low-salinity water flooding. This work examines an approach of characterizing porous media with machine learning algorithmns in Python, at the example of sandstone samples. Within in this scope, threedimensional computertomograms of the sandstone samples Bentheimer und Castelgate were recorded and the pore network was dissected into pore segments. The segmentation was done in two differenct approches. In the first cubes of defined edge length were cut out at random positions of the Tomogramm, thereby only the pore fraction was considered. In the second approach the pore network was systematically segmented with the Watershed algorithmn. The pore segments were quantified by the concept of Minkowsi Tensors using the software Bilimbi and used as features for the three machine learning algorithmns: neural network, c support vector machine and random forrest. Thereby the parameters of the algorithmns were optimized before the actual classification. Furthermore the influence of the different Minkowski Tensors on the classification was investigated using selection algorithmns. The results were verified by classifying the tomogramms based on the features with the highest influence. The presented method allows the examination of the influence of the pore network on the low-salinity effect of sandstones and the assassment of oil-bearing sandstone for the oil recovery technique of low-salinity water flooding.